Concerning that using camera and sensor to detect human motion has the shortcomings of difficult deployment, expensive device and blind zone, a method of human motion detection using Wireless Fidelity (WiFi) signal was proposed. Firstly, the wireless network card was used to receive the Channel State Information (CSI) of the WiFi in the detected environment. Secondly, the Local Outlier Factor (LOF) algorithm and the Hampel filter were used to remove the abnormal CSI data. After the frequency shift caused by the rough synchronization of the network card clock was removed by the linear regression algorithm, the Principal Component Analysis (PCA) was used to reduce dimension and Naive Bayes algorithm was used to classify the CSI data in different cases, which generated a model for judging human movement states. Finally, the model was used to judge the state of human motion. In the experiment, the proposed method can quickly determine the state of human motion and reach the correct rate of 95.62%. The experimental results show that the proposed method can detect and identify the movement of people well.